Trust and Personalization in E-Commerce: Key Strategies for Customer Engagement

1. E-Commerce – customer focus • Attracting and keeping customers • Key issue: trust, security • Legal issues • Personalization • Adverts

2. Customers are not all the same! • Consumer types • Individual consumers • Organizational buyers • Goal of shopping • Pragmatic: buy something useful, cheaply • Hedonistic: have fun • Personality • Impulsive buyers — purchase quickly • Patient buyers — make some comparisons first • Analytical buyers — do substantial research before buying

3. Consumer Behaviour Prentice Hall, 2002

4. Consumer Satisfaction Prentice Hall, 2002

5. Trust/Security • Trust/Security • Will the company actually deliver the correct product/service in reasonable shape, in a reasonable time, at correct price • Will the customer pay up (is the credit card stolen, will it be repudiated) • Technical aspects • Human aspects: Focus here on trust and, to some extent, policies

6. Trust in physical shops • Experience: shoppers trust shops they’ve used before • Appearance: shoppers trust store that look reputable • Complaints: easy to complain, shop can’t hide • Transactions are simple

7. On-line trust • What makes you trust an e-commerce shop?

8. On-line Trust • Experience: I trust Amazon because I’ve used them before • Reputation: because my friends use them • Very important with e-shops • Specific technicalities; for example, accounts/cards compromised or not? • Appearance: Do I trust Amazon because they have a nice website? • Less important than with physical shops • Marketing helps

9. On-line trust • Complaints: Harder to complain since don’t know where shop is • Transactions are complex because of delivery • Where many e-shops mess up • Third-party: do I trust Amazon more if another web site says good things about Amazon?

10. Does Amazon Trust Me? • Amazon trusts me because • Experience: I’ve always paid Amazon before • Reputation: I’ve used other companies and always paid up • Marketing: Amazon threatens nasty things to customers who don’t pay up

11. Trust • We know how trust is established in physical shops. • We are developing mechanisms for establishing trust in e-shops • Partially technology, but psychology and sociology probably matter more • Lack of trust mechanisms is barrier to new e-shops

12. Legal Issues: Tax • In USA, one driving force behind early e-store success was less tax • Because of a tax loophole, sales tax (VAT) was not charged on e-commerce sales • Automatically gave price advantage to e-commerce sites!

13. Legal Issues: Intl E-Commerce • In theory, e-commerce means sites can sell globally • In practice, difficult because of different tax rules, regulations, customs, etc • More common to set up subsidiaries in different countries, as Amazon has done • Lack of global legal/regulatory framework hinders ecommerce

14. Personalization • E-Commerce sites can treat customers differently • Offer recommendations, special deals • Personalise web site • Adjust prices • In theory, “personalised shop” one of the great benefits of e-commerce

15. One-to-One Marketing • Build a long term association • Meeting customers cognitive needs • Customer may have novice, intermediate or expert skill • E-loyalty—customer’s loyalty to an e-tailer • costs Amazon $15 to acquire a new customer • costs Amazon $2 to $4 to keep an existing customer • Trust in EC • Deterrence-based —threat of punishment • Knowledge-based —reputation • Identification-based —empathy and common values • Referrals – Viral Marketing • Personalisation…

16. Personalisation - Marketing Model“Treat different customers differently” Prentice Hall, 2002

17. Personalisation • “Process of matching content, services, or products to individuals’ preferences” • Build profiles – N.B. Privacy Issues • Solicit information from users • Use cookies to observe online behavior • Use data or Web mining

18. Recommendation • Build profiles • What has X bought? • What has X looked at? • Demographics: age, gender, etc • Recommendation • Rules: If X buys Harry Potter 6, recommend HP 7 • Data Mining: Other people who bought Harry Potter also bought Lord of the Rings • Collaborative: X’s overall buying profile is similar to Y, so recommend whatever Y bought

19. Automated prediction of trends and behaviors Example: from data on past promotional mailings, find out targets most likely to respond in future Automated discovery of previously unknown patterns Example: find seemingly unrelated products often purchased together Example: Find anomalous data representing data entry errors Mining tools: Neural computing Intelligent agents Association analysis - statistical rules Web Mining - Mining meaningful patterns from Web resources Web content mining – searching Web documents Web usage mining – searching Web access logs Data Mining searching for valuable information in extremely large databases

20. Recommendations • If done well, perceived very positively • Real benefit, not just marketing spam • Credit-card companies have done this well • Have the most purchasing data? • Data privacy issues • Can Visa sell data about you to Amazon? • Spyware to track all of your web browsing?

21. Personalise Web Sites • Let customers create their own “shop front” focusing on their interest • Adjust appearance (eg, for visually disabled, or strict Muslims) • Doable, not huge success

22. Personalised Pricing • Companies would love to be able to charge people different amounts for the same product • Airline seats, cars, etc • Full price for people who are keen, in a rush, don’t care about money • Discount for choosy/finicky

23. Personalised Pricing • Amazon, etc have tried this, but customers hated it. • So has gone “underground” for now. • Technology permits this, but society’s expectations does not allow it

24. Advertising • E-Shops (and other sites) can make money via advertising • Google makes billions from its “sponsored links” • Amazon has adverts as well

25. Web Advertising • Conventional advertising focuses on visual appeal • Less successful on web • Flashy animated banner adverts are a nuisance and distraction

26. Targeted adverts • Web allows relevant adverts to be associated with a web page • Google sponsored links based on search • Amazon could display different adverts for sci-fi and romance novel • Very effective if done well • So Web sites can charge more for targeted adverts

27. Web adverts • Initially treated like TV adverts, put huge effort into flashy multimedia banner ads • Now focusing on simple targeted adverts instead • Advertising models cannot be blindly moved from TV to web • need new models!

28. E-Commerce Summary • Initially tried to make e-shops similar to high street shops. But • Need different business model • Trust issues much more important • Need appropriate legal framework

29. E-Commerce Summary • Sometimes technology really helps • Recommender systems, targeted adverts • Sometimes technology works but society doesn’t like it • Differential pricing